Fifth and final day of the GRC Viz conference and my last post of the week. Today we heard from Doug Clark (Vanderbilt University) and Seth Cooper (University of Washington).

Doug Clark

We started with Doug Clark (Vanderbilt University) whose research involves students’ learning processes as they study core science concepts in digital environments and computer games. He showed us a few of the digital simulations – computational models where users determine the outcomes – that he and his group create. These are digital games where the learners make choices with meaningful implications, inside an overarching set of explict goals, with accompaning systems for measuring progress (progress tracking, scoring systems, badges, etc) and subjective opportunities for play and engagement. Clark has developed mostly little physics games that are at the intersection of digital games and digital simulations. Clark clarifies that digital games are to simulations as feature films are to animations.

The NRC report, Are Games Good for Learning, Clark suggests, asks the wrong question. Games are just a medium that has potential, constraints, and affordances just like any other medium….books, movies,microscopes….the better question is: which designs and structures optimize which outcomes for whom and how?

Clark explains that the best, most succesful games are complex. Players won’t buy games that are too simple. Good digital games help people construct productive mental models for pearting on the underlying simulations. He suggests checking out the Flash games on Koncregate.

Affordances of Good Digital Games

engagement

context

point of view (pathway)

stakes, investment

monitoring, feedback, pacing, gatekeeping

The models that underly Clark’s games are similar to models from other games that work well.

Clark reminded us about the way that Paul Gee (Arizona State University) talks the notion of “Game” (with a capital G), meaning the larger circle of community, practices, artifacts, and interactions around the game. In other words, all the learning doesn’t have to happen in the game itself. As one example, I was reminded of the wealth of game play videos that can be found online and the way the gaming community uses them to showcase what they know, document progress, and help others.

He showed us a couple of his games – Surge (“…saving the world, one fuzzy at a time”). It looks fun to play, challenging to master and it seems to connect intuitive and formal understanding through game play. He used the Force Concept Inventory (FCI) to assess students progress using the game and saw modest gains, but the outcomes were very equitable. That is, no gender differences and the outcomes were not correlated with reported gaming habits (good news). Players need to learn and use physics principles and representations to succeed in the game. Subsequent levels of the game aggregate concepts and require application.

Clark emphacized the importance of storyline in a well-designed game. Rescue and reuniting are the embedded storylines in many of their games.

They collect location data of every player as they play. This data is processed and then they use it to create graphical representations of players progress through the game. Analyzing these maps suggests ways that students might be assisted with scaffolding, during game play. I loved the idea of building a model of what the students know while they are playing.

Clark talked about the challenges in his work which will be addressed with future projects. He’s looking for methods for students to record their experiences without leaving the game – ways they can express their predictions and explanations fluidly within the game. He also hopes to devise methods to protect novice players from frustration as they learn the game (guidelines, visual cueing, just-in-time scaffolding, hints). As Clark explains it, you want people to fail early and often and then get beyond it.

He also spent some time talking about where the “challenge curve” should be. If you’re behind the curve in a game, you’re frustrated; if you’re ahead of it, you’re bored. Clark and his group eventually settled on broadening the curve to minimize the costs of failure, encourage improved performance through non-game-mechanic incentives (medals, badges, leader boards). The problem with giving game players advantages within the game, that have to do with game mechanics (e.g. new devices/weapons, tips or hints) is that the rich just get richer and the people who are slower just get more frustrated. They try to design games were the difficulty increases depending on accomplishments; games where there are multiple paths or solutions of varying difficulty and reward. This is informed by Jenova Chen’s concept of “flow” – when the task and th skill levels match up. The very heart of good game work – modulating the challenge so that the player is on the right part of the curve.

He’s built an open source environment called WISE where non-programmers can use elements through a menu system to build their own games and he is now building a Flash player front end to the environment. More to come there.

Next up was Seth Cooper, University of Washington, with a talk entitled “Foldit and Games for Scientific Discovery”. Seth talked about games as an ideal framework for motivating, collaborating, and competing – a natural problem solving space. They are also very adaptable – that is, the games can be adapted to the players, as they play it.

At the University of Washington, Cooper collaborated with scientists in biochemistry to make Foldit. Players work together to fold proteins. This is important to biological research because knowing the struture of a protein is key to knowing how it works. The really cool part of this is that the folding problems posted on Foldit are open problems. That is, structures that biochemists and molecular biologists need help figuring out (the number of ways any given protein can be folded are huge because there are so many open parameters). Over 100k players have posted several hundred protein folds to the site. The game is constantly evolving, in response to what the players are doing. Solutions are submitted, then analyzed, then posted for others to see. The players score is related to the energy of the structure (the better the folding solution, the higher the score). Interestingly, they don’t know what the highest score is. There is a leader board so that the players are competing against each other. Players can fix incorrectly folded proteins, adjust and look for better folding strategies.

Foldit

They have surveyed their players and found that they are not your typical gamer profile – wide and variant backgrounds (but mostly male). They also were not necessarily biochemists! For instance 3.4 of their top players had no more than a first course in biochemistry. The players have created their own strategy wiki in order to comment on each other’s strategies and share their thoughts and ideas. They also added a “cookbook” where recipes are shared for various moves and options and run them on their own proteins.

Interestingly, a number of protein scientists are using Foldit results in their research – with scientists and players working together to come up with solutions, test them, and design structures.

So, what’s next? Designing nanodevices made out of DNA. Digitizing the world – where players take pictures of a building, upload them, in a sort of capture-the-flag style game. As you take pictures, you capture flags, which motivates you to take more pictures. Multiple teams, competing against each other. Competitions between universities and expertise training. Very interesting stuff.